Synthetic Temporal Contact Networks Generated via Bayesian-Calibrated Human Mobility Models for Epidemic Simulations

This dataset contains high-resolution, synthetic temporal contact networks (TCNs) generated using Bayesian-optimized human mobility models (HuMMs) integrated into epidemic simulation framework MEmilio. The data support the results of the paper “Integrating Human Mobility Models with Epidemic Modeling: A Framework for Generating Synthetic Temporal Contact Networks” which introduces a scalable approach to generating empirically grounded contact networks across diverse environments including households, schools, workplaces, supermarkets, and social events.

The generated networks cover various population sizes (e.g., 1000, 2000, 5000 households) and environment capacities (small, medium, large), enabling the study of structural and dynamic epidemic properties under realistic assumptions. Each network consists of a sequence of contact graphs over time (hourly resolution), capturing the spatiotemporal interaction patterns of agents.

The datasets include:

Temporal edge lists for each generated TCN variant.

Agent metadata (e.g., age group, household assignment).

Location metadata (e.g. identifier).

These datasets are intended to support epidemic modeling research, particularly for evaluating the impact of non-pharmaceutical interventions and risk-based mitigation strategies in fine-grained, dynamic contact settings.